Enhancing Fixed Size Palmprint Region of Interest (ROI) Extraction Algorithm for Personal Identification
Nurzalina Harun1, Wan Eny Zarina Wan Abdul Rahman2, Sharifah Aliman3, Husna Ramena4, Nurain Syazana Othman5
1Nurzalina Harun*, Centre of Mathematics Studies, Faculty of Computer and Mathematical Sciences, MARA University of Technology, Shah Alam, Selangor, Malaysia.
2Wan Eny Zarina Wan Abdul Rahman, Centre of Mathematics Studies, Faculty of Computer and Mathematical Sciences, MARA University of Technology, Shah Alam, Selangor, Malaysia.
3Sharifah Aliman, Advanced Analytics Engineering Centre of Computer Sciences, Faculty of Computer and Mathematical Sciences, MARA University of Technology, Shah Alam, Selangor, Malaysia.
4Husna Ramena, Centre of Mathematics Studies, Faculty of Computer and Mathematical Sciences, MARA University of Technology, Shah Alam, Selangor, Malaysia.
5Nurain Syazana Othman, Centre of Mathematics Studies, Faculty of Computer and Mathematical Sciences, MARA University of Technology, Shah Alam, Selangor, Malaysia.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6918-6923 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5177118419/2019©BEIESP | DOI: 10.35940/ijrte.D5177.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300×300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms.
Keywords: Biometric, Creases, Extraction, Palmprint, Region of Interest (ROI)
Scope of the Article: Biomedical Computing.